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  • Jožef Stefan Institute

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  • Organisation: Jožef Stefan Institute
  • Language: Macedonian
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  • Word embeddings CLARIN.SI-embed.mk 2.0

    CLARIN.SI-embed.mk contains word embeddings induced from a large collection of Macedonian texts crawled from the .mk top-level domain. The embeddings are based on the skip-gram model of fastText trained on 933,231,582 tokens of running text for 986,670 lowercased surface forms. The difference to the previous version of the embeddings is that this version was trained on the original dataset expanded with the MaCoCu-mk web crawl corpus (http://hdl.handle.net/11356/1512).

  • Multilingual text genre classification model X-GENRE

    The X-GENRE classifier is a text classification model that can be used for automatic genre identification. The model classifies texts to one of 9 genre labels: Information/Explanation, News, Instruction, Opinion/Argumentation, Forum, Prose/Lyrical, Legal, Promotion and Other (refer to the provided README file for the details on the labels). The model was shown to provide high classification performance on Albanian, Catalan, Croatian, Greek, English, Icelandic, Macedonian, Slovenian, Turkish and Ukrainian, and the zero-shot cross-lingual experiments indicate that it will likely provide comparable performance on all other languages that are supported by the XLM-RoBERTa model (see Appendix in the following paper for the list of covered languages: https://arxiv.org/abs/1911.02116). The model is based on the base-sized XLM-RoBERTa model (https://huggingface.co/FacebookAI/xlm-roberta-base). It was fine-tuned on the training split of an English-Slovenian X-GENRE dataset (http://hdl.handle.net/11356/1960), comprising of around 1,800 instances of Slovenian and English texts. Fine-tuning was performed with the simpletransformers library (https://simpletransformers.ai/) and the following hyperparameters were used: Train batch size: 8 Learning rate: 1e-5 Max. sequence length: 512 Number of epochs: 15 For the optimum performance, the genre classifier should be applied to documents of sufficient length (the rule of thumb is at least 75 words), the predictions of label "Other" should be disregarded, and only predictions, predicted with confidence higher than 0.8, should be used. With these post-processing steps, the model was shown to reach macro-F1 scores of 0.92 and 0.94 on English and Slovenian test sets respectively (cross-dataset scenario), macro-F1 scores between 0.88 and 0.95 on Croatian, Macedonian, Turkish and Ukrainian, and macro-F1 scores between 0.80 and 0.85 on Albanian, Catalan, Greek, and Icelandic (zero-shot cross-lingual scenario). Refer to the provided README file for instructions with code examples on how to use the model.

  • The CLASSLA-Stanza model for lemmatisation of standard Macedonian 2.1

    The model for lemmatisation of standard Macedonian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the 1984 training corpus expanded with the Macedonian SETimes corpus (to be published). The estimated F1 of the lemma annotations is ~98.81. The difference from the previous version is that this version was trained using a larger training dataset.

  • The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Macedonian 1.1

    This model for morphosyntactic annotation of standard Macedonian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the 1984 training corpus (to be published) and using the Macedonian CLARIN.SI word embeddings (http://hdl.handle.net/11356/1359). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~97.6. The difference to the previous version of the model is that the pre-trained embeddings are limited to 250 thousand entries and adapted to the new code base.

  • The CLASSLA-Stanza model for morphosyntactic annotation of standard Macedonian 2.1

    This model for morphosyntactic annotation of standard Macedonian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the 1984 training corpus expanded with the Macedonian SETimes corpus (to be published) and using the Macedonian CLARIN.SI word embeddings (http://hdl.handle.net/11356/1788). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~97.14. The difference from the previous version is that this version was trained using a larger training dataset and the new version of the Macedonian word embeddings.

  • The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Macedonian 1.0

    This model for morphosyntactic annotation of standard Macedonian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the 1984 training corpus (to be published) and using the Macedonian CLARIN.SI word embeddings (http://hdl.handle.net/11356/1359). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~97.6.